[Enhance] support semantic seg in Flip3D augmentation (#1181)

* support semantic seg in flip

* change box_dtype_3d to bbox3d_fields

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -112,6 +112,10 @@ class RandomFlip3D(RandomFlip):
                 updated in the result dict.
         """
         assert direction in ['horizontal', 'vertical']
+        # for semantic segmentation task, only points will be flipped.
+        if 'bbox3d_fields' not in input_dict:
+            input_dict['points'].flip(direction)
+            return
         if len(input_dict['bbox3d_fields']) == 0:  # test mode
             input_dict['bbox3d_fields'].append('empty_box3d')
             input_dict['empty_box3d'] = input_dict['box_type_3d'](
@@ -148,7 +152,7 @@ class RandomFlip3D(RandomFlip):
                 'pcd_horizontal_flip' and 'pcd_vertical_flip' keys are added \
                 into result dict.
         """
-        # filp 2D image and its annotations
+        # flip 2D image and its annotations
         super(RandomFlip3D, self).__call__(input_dict)
 
         if self.sync_2d:
@@ -916,11 +920,11 @@ class PointSample(object):
         """
         points = results['points']
         # Points in Camera coord can provide the depth information.
-        # TODO: Need to suport distance-based sampling for other coord system.
+        # TODO: Need to support distance-based sampling for other coord system.
         if self.sample_range is not None:
             from mmdet3d.core.points import CameraPoints
             assert isinstance(points, CameraPoints), \
-                'Sampling based on distance is only appliable for CAMERA coord'
+                'Sampling based on distance is only applicable for CAM coord'
         points, choices = self._points_random_sampling(
             points,
             self.num_points,
@@ -1288,7 +1292,7 @@ class VoxelBasedPointSampler(object):
     Args:
         cur_sweep_cfg (dict): Config for sampling current points.
         prev_sweep_cfg (dict): Config for sampling previous points.
-        time_dim (int): Index that indicate the time dimention
+        time_dim (int): Index that indicate the time dimension
             for input points.
     """
 
@@ -1312,7 +1316,7 @@ class VoxelBasedPointSampler(object):
             points (np.ndarray): Points subset to be sampled.
             sampler (VoxelGenerator): Voxel based sampler for
                 each points subset.
-            point_dim (int): The dimention of each points
+            point_dim (int): The dimension of each points
 
         Returns:
             np.ndarray: Sampled points.
@@ -1393,7 +1397,7 @@ class VoxelBasedPointSampler(object):
             points_numpy = points_numpy.squeeze(1)
         results['points'] = points.new_point(points_numpy[..., :original_dim])
 
-        # Restore the correspoinding seg and mask fields
+        # Restore the corresponding seg and mask fields
         for key, dim_index in map_fields2dim:
             results[key] = points_numpy[..., dim_index]